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Record W2119045235 · doi:10.1038/clpt.2009.194

Selective Antagonism of Opioid-Induced Ventilatory Depression by an Ampakine Molecule in Humans Without Loss of Opioid Analgesia

2009· article· en· W2119045235 on OpenAlex
B. Oertel, Lisa Felden, P.V. Tran, Mark Bradshaw, Martin S. Angst, Harald Schmidt, Steve A. Johnson, John J. Greer, Gerd Geißlinger, Mark A. Varney, Jörn Lötsch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAlfentanilOpioidAnesthesia(+)-NaloxoneMedicinePlaceboDepression (economics)Clinical pharmacologyVentilation (architecture)KetaminePharmacologyPropofolInternal medicineReceptor

Abstract

fetched live from OpenAlex

Ventilatory depression is a significant risk associated with the use of opioids. We assessed whether opioid-induced ventilatory depression can be selectively antagonized by an ampakine without reduction of analgesia. In 16 healthy men, after a single oral dose of 1,500 mg of the ampakine CX717, a target concentration of 100 ng/ml alfentanil decreased the respiratory frequency by only 2.9 +/- 33.4% as compared with 25.6 +/- 27.9% during placebo coadministration (P < 0.01).Blood oxygenation and the ventilatory response to hypercapnic challenge also showed significantly smaller decreases with CX717 than with placebo. In contrast, CX717 did not affect alfentanil-induced analgesia in either electrical or heat-based experimental models of pain. Both ventilatory depression and analgesia were reversed with 1.6 mg of naloxone. These results support the use of ampakines as selective antidotes in humans to counter opioid-induced ventilatory depression without affecting opioid-mediated analgesia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.428
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it